{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Navigation\n",
    "\n",
    "---\n",
    "\n",
    "You are welcome to use this coding environment to train your agent for the project.  Follow the instructions below to get started!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Start the Environment\n",
    "\n",
    "Run the next code cell to install a few packages.  This line will take a few minutes to run!\n",
    "Run the next code cell to install a few packages.  This line will take a few minutes to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mtensorflow 1.7.1 has requirement numpy>=1.13.3, but you'll have numpy 1.12.1 which is incompatible.\u001b[0m\r\n",
      "\u001b[31mipython 6.5.0 has requirement prompt-toolkit<2.0.0,>=1.0.15, but you'll have prompt-toolkit 3.0.5 which is incompatible.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip -q install ./python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment is already saved in the Workspace and can be accessed at the file path provided below.  Please run the next code cell without making any changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install unityagents if not installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install unityagents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import other packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: BananaBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 37\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: discrete\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "\n",
    "# please do not modify the line below\n",
    "env = UnityEnvironment(file_name=\"/data/Banana_Linux_NoVis/Banana.x86_64\")\n",
    "\n",
    "import os\n",
    "from collections import deque\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 1\n",
      "Number of actions: 4\n",
      "States look like: [ 1.          0.          0.          0.          0.84408134  0.          0.\n",
      "  1.          0.          0.0748472   0.          1.          0.          0.\n",
      "  0.25755     1.          0.          0.          0.          0.74177343\n",
      "  0.          1.          0.          0.          0.25854847  0.          0.\n",
      "  1.          0.          0.09355672  0.          1.          0.          0.\n",
      "  0.31969345  0.          0.        ]\n",
      "States have length: 37\n"
     ]
    }
   ],
   "source": [
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents in the environment\n",
    "print('Number of agents:', len(env_info.agents))\n",
    "\n",
    "# number of actions\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Number of actions:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "state = env_info.vector_observations[0]\n",
    "print('States look like:', state)\n",
    "state_size = len(state)\n",
    "print('States have length:', state_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Take Random Actions in the Environment\n",
    "\n",
    "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
    "\n",
    "Note that **in this coding environment, you will not be able to watch the agent while it is training**, and you should set `train_mode=True` to restart the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "env_info = env.reset(train_mode=True)[brain_name] # reset the environment\n",
    "state = env_info.vector_observations[0]            # get the current state\n",
    "score = 0                                          # initialize the score\n",
    "while True:\n",
    "    action = np.random.randint(action_size)        # select an action\n",
    "    env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "    next_state = env_info.vector_observations[0]   # get the next state\n",
    "    reward = env_info.rewards[0]                   # get the reward\n",
    "    done = env_info.local_done[0]                  # see if episode has finished\n",
    "    score += reward                                # update the score\n",
    "    state = next_state                             # roll over the state to next time step\n",
    "    if done:                                       # exit loop if episode finished\n",
    "        break\n",
    "    \n",
    "print(\"Score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. It's Your Turn!\n",
    "\n",
    "Now it's your turn to train your own agent to solve the environment!  A few **important notes**:\n",
    "- When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
    "```python\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "```\n",
    "- To structure your work, you're welcome to work directly in this Jupyter notebook, or you might like to start over with a new file!  You can see the list of files in the workspace by clicking on **_Jupyter_** in the top left corner of the notebook.\n",
    "- In this coding environment, you will not be able to watch the agent while it is training.  However, **_after training the agent_**, you can download the saved model weights to watch the agent on your own machine! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dqn_agent import Agent, QNetwork\n",
    "from dqn import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = Agent(state_size=state_size, action_size=action_size, seed=0, mode='Double_DQN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100\tAverage Score: 1.68\tepsilon: 0.04755\n",
      "Episode 200\tAverage Score: 6.15\tepsilon: 0.00500\n",
      "Episode 300\tAverage Score: 10.97\tepsilon: 0.00500\n",
      "Episode 400\tAverage Score: 12.79\tepsilon: 0.00500\n",
      "Episode 405\tAverage Score: 13.00\n",
      "Environment solved in 405 episodes!\tAverage Score: 13.00\n"
     ]
    }
   ],
   "source": [
    "scores = dqn(n_episodes=500,eps_end = .005,eps_decay=.97)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e91555be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "n_episodes = len(scores)\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(np.linspace(1,n_episodes+1, n_episodes),scores)\n",
    "ax.set_xlabel('Episode')\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_title('Score over episode of DQN model');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " tar -zcvf archive.tar.gz * \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
